# dose.p

From MASS v7.3-51.4
by Brian Ripley

##### Predict Doses for Binomial Assay model

Calibrate binomial assays, generalizing the calculation of LD50.

- Keywords
- models, regression

##### Usage

`dose.p(obj, cf = 1:2, p = 0.5)`

##### Arguments

- obj
A fitted model object of class inheriting from

`"glm"`

.- cf
The terms in the coefficient vector giving the intercept and coefficient of (log-)dose

- p
Probabilities at which to predict the dose needed.

##### Value

An object of class `"glm.dose"`

giving the prediction (attribute
`"p"`

and standard error (attribute `"SE"`

) at each response
probability.

##### References

Venables, W. N. and Ripley, B. D. (2002)
*Modern Applied Statistics with S.*
Springer.

##### Examples

```
# NOT RUN {
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20 - numdead)
budworm.lg0 <- glm(SF ~ sex + ldose - 1, family = binomial)
dose.p(budworm.lg0, cf = c(1,3), p = 1:3/4)
dose.p(update(budworm.lg0, family = binomial(link=probit)),
cf = c(1,3), p = 1:3/4)
# }
```

*Documentation reproduced from package MASS, version 7.3-51.4, License: GPL-2 | GPL-3*

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